Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.

Journal: JCO clinical cancer informatics
Published Date: Apr 1, 2024

Abstract

PURPOSE: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is critical for characterizing the benefit/risk profile of ICI therapies beyond the clinical trial population. Diagnosis codes, such as International Classification of Diseases codes, do not comprehensively illustrate a patient's care journey and offer no insight into drug-irAE causality. This study aims to capture the relationship between ICIs and irAEs more accurately by using augmented curation (AC), a natural language processing-based innovation, on unstructured data in electronic health records.

Authors

  • Hannah Barman
    nference, Cambridge, MA.
  • Sriram Venkateswaran
    F. Hoffmann-La Roche, Basel, Switzerland.
  • Antonio Del Santo
    F. Hoffmann-La Roche, Basel, Switzerland.
  • Unice Yoo
    nference, Cambridge, MA.
  • Eli Silvert
    nference, Cambridge, MA.
  • Krishna Rao
    nference, Cambridge, MA.
  • Bharathwaj Raghunathan
    nference, Cambridge, MA.
  • Lisa A Kottschade
    Department of Oncology, Mayo Clinic, Rochester, MN.
  • Matthew S Block
    Department of Oncology, Mayo Clinic, Rochester, MN.
  • G Scott Chandler
    F. Hoffmann-La Roche, Basel, Switzerland.
  • Joshua Zalis
    nference, Cambridge, MA.
  • Tyler E Wagner
    nference, Cambridge, MA.
  • Rajat Mohindra
    F. Hoffmann-La Roche, Basel, Switzerland.

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